System and Method of Intelligent Vegetation Management

2. The system of claim 1, wherein the aerial images taken during a first duration of time are satellite images taken in a particular month of a particular year.

3. The system of claim 1, the location information being provided by third-party over the communication network, the third-party that manages the assets, the third-party being remote and separate from the first image source, the location information including coordinates of the assets.

4. The system of claim 3, wherein the memory contains instructions to control the one or more of the at least one processor to further correlate the location information with metadata associated with the one or more images of the aerial images to determine the likely location of the at least on asset within the each image of the first set of aerial images, the metadata including coordinates of at least some of the geographic area.

5. The system of claim 1, wherein determining the at least one zone includes determining a first zone of the at least one zone based on one or more possible hazardous conditions that may be caused by the one or more obstructions on the at least one asset, determining the first zone including determining a width of the at least one zone based on the one or more possible hazardous conditions.

6. The system of claim 5, wherein the one or more possible hazardous conditions are weighted based on terrain type of the geographic area, weather patterns, and accessibility of the geographic area.

7. The system of claim 6, wherein the one or more possible hazardous conditions are weighted based on risk of wildfire.

9. The system of claim 1 wherein the convolutional neural network is a u-net convolutional neural network.

11. The method of claim 10, wherein the aerial images taken during a first duration of time are satellite images taken in a particular month of a particular year.

12. The method of claim 10, the location information being provided by third-party over the communication network, the third-party that manages the assets, the third-party being remote and separate from the first image source, the location information including coordinates of the assets.

13. The method of claim 12, the method further comprising correlating the location information with metadata associated with the one or more images of the aerial images to determine the likely location of the at least on asset within the each image of the first set of aerial images, the metadata including coordinates of at least some of the geographic area.

14. The method of claim 10, wherein determining the at least one zone includes determining a first zone of the at least one zone based on one or more possible hazardous conditions that may be caused by the one or more obstructions on the at least one asset, determining the first zone including determining a width of the at least one zone based on the one or more possible hazardous conditions.

15. The method of claim 14, wherein the one or more possible hazardous conditions are weighted based on terrain type of the geographic area, weather patterns, and accessibility of the geographic area.

16. The method of claim 14, wherein the one or more possible hazardous conditions are weighted based on risk of wildfire.

18. The method of claim 10, wherein the convolutional neural network is a u-net convolutional neural network.